Search Results (170)

Despite an ageing society emerging as a global challenge, urban spaces still do not adequately address the spatial needs of older citizens. Numerous studies analyse built environment characteristics in relation to the mobility of older citizens, yet studies on the quality of older pedestrians’ perception of spatial attributes with their levels are scarce. This scoping review of 2855 records from 2013 to 2023, exported from Scopus and Web of Science, aimed to identify common patterns with respect to the aspects used in the assessment of the quality of urban spaces for older adults, with the emphasis placed on spatial attributes measured through different levels. Following PRISMA-ScR, the analysis was conducted in AsReview, a scientific tool using ML. Inclusion criteria were: peer-reviewed English-language journal articles and conference papers; the inclusion of spatial attributes in urban planning, measuring the perception of pedestrians, using a conjoint experiment, or urban digital twins; and taking into account an ageing society. The author performed the coding of 115 eligible records in four iterative rounds with the use of Atlas.ti. The findings show that Land Use & Buildings/Destinations, Sidewalk and Amenities, and Aesthetics/Urban Form were the most frequently occurring aspects. Attribute levels were proposed only in 10 records. No study incorporated stated preference and 3D walk-through environments to quantify older adults’ perception of walkability-related attributes. This represents a methodological gap for future research on older adults’ walkability perception. Urban planners and other decision-makers may use the findings of this study to support the design and management of age-friendly, sustainable, and inclusive street environments. Full article

Road traffic noise in urban streets is shaped not only by traffic sources but also by sound propagation through the surrounding street geometry. Existing prediction methods are still largely source-oriented, and receptor-specific models that rely on street morphology alone remain uncommon. We developed and compared interpretable machine-learning models to predict a cyclist-side sound pressure level (SPL) under fixed source conditions, using 12 spatial parameters extracted from 5060 street sections on 195 streets in Harbin, China. Acoustic simulations were performed in ODEON under fixed source-power conditions, and four models—Linear Regression, support vector regression (SVR), extreme gradient boosting (XGBoost), and Random Forest (RF)—were evaluated through an illustrative 80/20 split, 20 repeated random 80/20 splits, and 20 road-name-based grouped holdout repetitions. The nonlinear models consistently outperformed the linear baseline. Under grouped holdout validation, XGBoost achieved the highest predictive accuracy (R² = 0.953 ± 0.018, RMSE = 0.583 ± 0.119 dB, MAE = 0.418 ± 0.082 dB). RF reached comparable accuracy (R² = 0.938 ± 0.041, RMSE = 0.662 ± 0.210 dB, MAE = 0.453 ± 0.128 dB) and was retained for the interpretation of feature importance and marginal response patterns. A computation-time comparison based on 93 representative ODEON simulations showed that ODEON required a median of 2 min 33 s per street section, whereas the trained models predicted all 5060 sections in 0.013 s with XGBoost and 0.143 s with RF. The RF-based interpretation identified vehicle-lane width, sidewalk width, and near-zone cross-sectional enclosure degree as the most influential variables. Width-related parameters dominated cyclist-side SPL prediction, while enclosure-related parameters became more relevant mainly under narrower width conditions. The framework is therefore intended as a comparative morphology-screening tool under fixed source conditions, not as a predictor of real-world traffic noise under varying traffic states. Full article

Generative AI has advanced rapidly in architectural design; however, existing building footprint generation models tend to emphasize stylistic exploration while insufficiently integrating site context as a fundamental physical constraint that facilitates alignment with the surrounding urban fabric. To address this limitation, this study proposes a context-responsive methodology for generating building footprints using a multi-layered four-channel representation of site conditions—including roads, sidewalks, adjacent buildings, and site boundaries—within a Latent Diffusion Model framework. The proposed approach encodes these physical conditions into a structured tensor and concatenates them directly to the U-Net input, enabling site context to function as an explicit spatial control variable during generation. An ablation study evaluated the effectiveness of the proposed contextual configuration. Compared with a single-channel model, the four-channel model achieved an 18.08% reduction in average pixel-wise information entropy, indicating a measurable decrease in generative uncertainty. Qualitative analyses further demonstrated that the enriched contextual input promotes geometrically coherent footprint configurations, such as context-responsive setbacks and spatial alignment with surrounding built forms. These findings suggest that structured multi-channel site information enhances contextual grounding in generative design processes and may contribute to more environmentally integrated and spatially coherent architectural outcomes. Full article

Children’s everyday routes to school are increasingly recognised as important environments shaping physical and mental well-being. Yet, their emotional dimension remains insufficiently integrated into health-oriented urban design research, particularly in cities without formalised School Street policies. This study examines how children in Belgrade perceive and emotionally experience their everyday school routes and how such evidence can inform context-sensitive urban design. A mixed-method, child-centred participatory approach was applied with primary school pupils, combining participatory evaluation boards, cognitive route mapping, photo documentation, and facilitated classroom discussion. The material was analysed through qualitative coding, triangulation, and a health-oriented reinterpretation of the SCORELINE framework (h_SCORELINE). The findings reveal recurring stress nodes associated with traffic-dominated streets, complex crossings, obstructed sidewalks, and poorly legible route segments, which children linked to fear, discomfort, and insecurity. By contrast, greenery, recognisable landmarks, visually calm environments, and wider pedestrian spaces emerged as joy nodes associated with comfort, enjoyment, and emotional ease. These patterns suggest that children’s emotional-spatial evidence can enrich the assessment of school-route environments beyond conventional traffic indicators alone. By linking children’s lived experiences with health-oriented urban design, the study provides evidence-based support for the gradual introduction of School Streets in Belgrade. It offers a transferable framework for integrating child-centred experiential knowledge into healthier street design. Full article

Urbanization and climate change intensify urban heat islands and air pollution; therefore, street canyon building planning that accounts for road orientation, shading, thermal environment, and ventilation is crucial. This study uses numerical simulations to investigate how non-uniform wall and road heating affects airflow and pollutant dispersion in street canyons under varying Richardson numbers (Ri) and heating scenarios (windward wall, leeward wall, road surface). The results indicate that large wall–atmosphere temperature differences combined with low incoming wind speed (high Ri) make thermal buoyancy a dominant control on canyon flow and pollutant transport. Heating of the leeward wall and road surface enhances ventilation and pollutant removal (prominently when the Ri ≥ 0.49), whereas heating of the windward wall suppresses dispersion and increases concentrations (prominently when the Ri ≥ 0.12). For a north–south street, diurnal solar heating produces strong micro-environmental contrasts. With easterly winds, morning heating of the windward wall elevates pollutant levels, while afternoon heating of the leeward wall promotes dispersion and lowers concentrations. Specifically, compared with the isothermal condition, the turbulent exchange rate at the top of the street canyon is enhanced to 1.71~6.86 times, while the convective exchange rate is suppressed to 58%~83% in the morning and enhanced to 1.21~1.92 times. These findings suggest that urban planning should limit windward wall temperature rises via shading and greening; thus, single-sided sidewalk and greening layouts on the windward side are recommended. Full article

Agentic artificial intelligence—systems that reason, plan, and act autonomously within governed workflows—is converging with autonomous electric mobility and urban robotics to reshape how cities govern, move, and manage physical space. We argue that the simultaneous arrival of these three vectors is triggering a transformation comparable in scope to the Industrial Revolution. Cities that deploy across all three domains are becoming the new hubs of innovation: they concentrate talent, accelerate knowledge circulation, enable cross-fertilisation, and generate hybrid proposals that no single vector could produce alone. Just as Manchester, Birmingham, and the Ruhr became the defining centres of industrialisation because steam, textiles, iron, and coal recombined through the proximity of the engineers and entrepreneurs who moved between them, a small number of cities today are pulling ahead because they host the shared talent pool around which agentic governance, autonomous mobility, and urban robotics co-evolve. Conceptually, we extend the mirroring hypothesis in two directions: dynamically, arguing that organisations and urban ecosystems converge toward the configurations new technologies make possible; and ontologically, arguing that agentic AI introduces non-human agents into organisational architectures, requiring hybrid human–AI coordination. We formalise this dynamic as five propositions (P1–P5) of cumulative recursive hybridisation (CRH), operating through four reinforcing feedback loops—data, regulation, infrastructure, and talent. Together, these loops explain why the emerging urban order is path-dependent: early movers accumulate compounding advantages, while latecomers face exponentially rising costs of entry. We demarcate CRH from adjacent frameworks—general-purpose technologies, organisational complementarities, and complex adaptive systems—and test it against counterfactual evidence from failed, stalled, and Global South trajectories (Sidewalk Toronto, the Cruise rollback, Songdo, Bengaluru). We also examine its political-economy, equity, and surveillance limits. Drawing on comparative evidence from public-sector chatbot deployments, autonomous mobility ecosystems in the United States and China, and emerging urban robotics cases, we conclude that what is at stake is not incremental modernisation but the construction of a new urban order. The cities that act as innovation hubs for the agentic AI era will shape global standards, attract global talent, and define the institutional templates that others eventually adopt—much as the industrial cities of the eighteenth and nineteenth centuries did. Full article

With accelerating global urbanization and increasingly diverse demands for public spaces, promoting urban low-carbon transitions and enhancing residents’ quality of life have become central missions of modern urban development. As one of the city’s primary arteries, streets—through their green landscapes, slow-moving transportation systems, and public facilities—play an indispensable role in reducing carbon emissions, promoting healthy living, and improving residents’ well-being. In this study, the Yubei District of Chongqing was selected as the research area, and an automated evaluation framework was proposed for street visual quality, based on multi-source street view data and ensemble learning. PSP-Net semantic segmentation model was employed to extract eight key visual indicators from street view images, including green view index, Visual Entropy (Entropy), sky view factor (SVF), drivable space, sidewalk, safety facilities, buildings, and enclosure. Based on these features, a Stacking-based ensemble learning model was constructed, integrating multiple base models such as Random Forest, XGBoost, and LightGBM, with Linear Regression as the meta-learner, to predict street visual quality. The results demonstrate that the ensemble model significantly outperforms any single model, achieving a correlation coefficient (r) of 0.77 and effectively capturing the complex perceptual features of street environments. This study provides a reliable, intelligent, and quantitative method for large-scale evaluation of urban street visual quality, while supplying data support and decision-making references for street renewal and spatial optimization. Full article

Urban living streets are core venues for promoting public health; however, existing studies often lack a multidimensional quantitative evaluation system that integrates physical, psychological, and social health dimensions. To address this gap, this study constructs a space quality evaluation model comprising 15 indicators across three health dimensions, integrating multi-source data (including Street View Imagery, POI data, and field measurements). Taking six typical living streets in the central urban area of Changsha as a case study, we applied the Analytic Hierarchy Process to determine indicator weights and evaluate space quality. The results reveal significant spatial heterogeneity: (1) The comprehensive quality scores vary markedly, with Cai’e South Road ranking highest (66.62) and Zengjiawan Lane lowest (28.37); (2) key factor analysis indicates that seven indicators—including Street Width, Motorization Level, and POI Functional Diversity—are significantly associated with space quality, among which Sidewalk Width and Relative Sidewalk Width are identified as critical determinants; (3) addressing identified deficits in slow-traffic spaces and service amenities, this study proposes health-oriented micro-renewal strategies. This study provides a transferable analytical framework and practical decision support for the assessment and improvement of urban living street space quality. Full article

As urban populations age, the built environment becomes a vital determinant of health equity. This research evaluates the sidewalk infrastructure, surrounding the Health Center in Egaleo, Greece, in order to quantify its impact on healthcare accessibility for older adults. Using a GIS-based approach to simulate realistic navigation, a routing algorithm prioritized the “easiest” path over the shortest distance by transforming accessibility scores into traversal costs. The results revealed a significant disadvantage in healthcare access, with routes to the Health Center scoring lower than the average accessibility of the greater study area. In addition, the negative correlation (r = −0.20, p < 0.001) confirms the pattern of accessibility disparity, where neighborhoods with the highest older adult density consistently face the poorest infrastructure. Eventually, Global Moran’s I of 0.912 confirms strong spatial autocorrelation, Local Indicators of Spatial Association (LISA) identifies “Accessibility Deserts” which comprise a 92.5% absence of crosswalks and an 81.7% rate of obstructions. This study outlines that those who depend most on the sidewalk network are disproportionately affected by inadequate urban planning conditions. By underscoring the necessity to remediate these low-accessibility clusters, public health is improved, ensuring equitable healthcare access and supporting healthy aging. Full article

How closely do the subjective perceptions simulated by Artificial Intelligence align with the subjective perceptions of human participants when evaluating an urban environment? This study serves as a pilot investigation to explore how far multimodal Large Language Models can effectively model human responses to visual stimuli based on subjective criteria. The exploratory nature of this research intends to test the feasibility of the methodology rather than provide a definitive standard. By focusing on a small set of detailed audits, a small-scale experiment performs an in-depth, qualitative examination of how machines and human assessments compare to each other in specific situations. To conduct the comparison, ratings of urban scenes were collected from human participants and two multimodal Large Language Models: ChatGPT and Gemini. After showing them an image of a sidewalk, these appraisers used a set of proposed statements to rate three sidewalks on a Likert scale. The investigation focuses on seven statements that subjectively characterize walkability factors, overall friendliness of an area, and the environment’s influence on well-being. Each participant rated each image once for all statements to establish a human baseline. The algorithms’ scores were generated using the exact same prompt, repeated multiple times to account for non-determinism. We then compared the AI’s scores to the humans’ distribution of scores and evaluated their alignment according to different experiential qualities across diverse visual environments. Full article

Wheelchair users face environmental barriers that limit their mobility and social participation. Although existing navigation tools support urban mobility, they often lack clear orientation and confirmation cues, and information on accessible and safe routes to meet wheelchair users’ needs. This study aims to identify the most adapted route instructions for wheelchair users, examine characteristics’ (sociodemographic information and profiles) impact on their instructions’ choices, and evaluate instruction’s delivery modalities. An online questionnaire collected participants’ characteristics and agreement with the proposed route instruction formulations (different combinations of information like turn-by-turn instructions, landmarks, and accessibility information) regarding clarity, sufficiency, adaptability, and safety criteria. Formulations were evaluated across 14 navigation situations involving accessibility and safety challenges. Participants also rated communication modalities. 32 wheelchair-users (19 males, 13 females; mean age = 45.8 years; mean wheelchair experience = 23.5 years) participated. Data analysis reveals the importance of enriched turn-by-turn instructions, including non-turning actions, alerts, landmarks, and/or street names for participants. Alert-based formulations were favored in most situations, like uneven sidewalks, slopes and intersections. More enriched instructions were significantly acceptable among women and participants with greater wheelchair experience. Multimodal delivery, particularly visual and audio information, was also preferred. These findings help develop adaptive navigation tools, improving wheelchair users’ safe, confident mobility, autonomy, and social participation. Full article

Reliable and up-to-date measurements of public space remain scarce in small and medium-sized towns (SMSTs), where conventional geospatial datasets are often outdated, inconsistent, or inaccessible. This study presents a low-cost and fully reproducible computational pipeline that integrates nadir RGB imagery captured by a DJI Mini 3 UAV with a lightweight instance-segmentation model (Ultralytics YOLOv12-seg) and GIS-based post-processing to derive class-specific surface indicators at the neighborhood scale. The workflow consists of four components: autonomous UAV acquisition over three representative zones of Sesquilé, Colombia; planar mosaic generation and georeferencing using ad hoc ground control points; fine-tuning of a YOLOv12-seg model trained on locally annotated images; and transformation of predicted masks into OSM and GeoPackage geometries for metric analysis. The trained model achieved stable convergence with mask mAP50 ≈ 0.85 and mAP50–95 ≈ 0.70, supported by balanced precision–recall behavior across classes. Spatial outputs exhibit coherent morphological contrasts between the analyzed zones. Buildings occupy 48.17% of the mapped area, vegetation 25.88%, and transport- and plaza-related public space (roadways, sidewalks, and hardscape areas) 25.95%. These proportions capture a clear gradient from a dense urban core to less consolidated peripheral sectors. Results demonstrate that very-high-resolution UAV imagery, combined with open-source deep-learning tools and structured GIS post-processing, can reliably produce operational public-space indicators for SMSTs at low cost. The methodology provides an accessible and scalable framework for evidence-based urban assessment in municipalities with limited technical and financial resources. Full article

Proximity-oriented urban models, such as the 15-min city, have been promoted to create sustainable, human-centered urban environments that support wellbeing. However, proximity alone does not guarantee accessibility, particularly for persons with disabilities. This paper explores how persons with disabilities experience and navigate Madrid (Spain) and Munich (Germany) under the proximity-oriented policies prism. Drawing on 114 semi-structured interviews (65 in Madrid, 49 in Munich), the study explores how urban form, design features, and environmental conditions shape access, movement, and engagement in public space. Findings reveal that key barriers, such as irregular paving and sidewalk obstructions, limit independence and comfort, while contextual factors such as climate, topography, and local cultural practices further modulate accessibility. Despite proximity, many participants remain reliant on cars instead of public transport due to these micro-scale barriers. By integrating proximity planning, inclusive urban experiences and universal design, this study highlights the need to move from “proximity as distance” to “proximity as accessible experience”, arguing that accessibility must be embedded as a structuring condition of proximity planning. Ultimately, these findings contribute to ongoing debates on sustainable built environments and human wellbeing, highlighting the importance of architectural and urban design in fostering equitable, healthy, and inclusive cities. Full article

Despite advancements in navigation apps for wheelchair users, there is no consensus on which environmental factors to prioritize for personalized accessible routes. This scoping review synthesizes factors influencing wheelchair mobility in urban settings, evaluates measurement methods, and assesses their integration into routing algorithms. Following Arksey and O’Malley’s framework and PRISMA-ScR guidelines, we analyzed six databases for English-language articles from 2005 to 2023, supplemented by an updated search covering 2023 to 2026. Two reviewers screened 6966 records and examined 79 full-text articles, with 24 meeting the inclusion criteria for data extraction. Environmental factors were categorized into static and dynamic factors affecting mobility. Key components included sidewalks (96%), ramps (63%), curb cuts (54%), stairs (50%), crosswalks (50%), and streets (38%). Common factors examined were length, slope, width, and surface properties. Data collection methods varied: 42% relied on measurements, 8% used user assessments and sensors, while 50% combined both approaches. Recent studies (2023–2026) demonstrate increasing adoption of AI and machine learning techniques, including crowdsourced smartphone data and generative AI for feature detection. This review identifies essential factors for wheelchair navigation and highlights significant gaps in dynamic factor assessment and real-time data integration. Full article

Saudi Arabia’s ambition to improve quality of life is paving its way, and this study aligns with that vision, adopting an experimental approach to explore urban solutions to enhance outdoor thermal comfort for pedestrians in neighborhoods within Riyadh City, Saudi Arabia. Given the city’s hot and arid climate, outdoor spaces are often subject to extreme thermal conditions that reduce the quality of life for residents. To address this issue, the study utilizes Ladybug in Grasshopper, a tool designed for modeling the microclimate and assessing the impact of urban design strategies on outdoor thermal comfort. A base model representing the current urban fabric of selected neighborhoods is developed, and then multiple alternatives of urban morphology (sidewalk, setbacks, fence, and vegetation) are evaluated for their effectiveness in mitigating heat stress and improving outdoor thermal conditions. The findings from this study provide valuable insights into how urban planning and design interventions can be tailored to the unique climatic challenges of Riyadh, with potential applications for enhancing the sustainability, livability, and overall quality of life of the city’s neighborhoods. Full article